[rgb]0,0,0Although VAD has been researched for years,
developing a model to detect anomalies in videos remains challenging, as it requires the model to understand the inherent differences between normal and abnormal events, especially anomalous events that are rare and vary substantially.
Previous works treat VAD as an unsupervised learning task [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]} [rgb]0,0,0, which
[rgb]0.,0.,0 encodes the usual pattern with only normal training samples, and then detects the distinctive encoded patterns as anomalies.
Here, we aim to address the weakly supervised video anomaly detection (WS-VAD) problem [8]}, [9]}, [10]}, [11]}, [12]} [rgb]0,0, 0because obtaining video-level labels is more realistic and can produce more reliable results than unsupervised methods.
More specifically, existing methods in WS-VAD can be categorized into two classes,
encoder-agnostic and encoder-based methods.
